Choose the autoregressive order of MixAR components

Reversible Jump MCMC algorithm to choose the optimal autoregressive order of each component of a mixture autoregressive model.

Usage

Choose_pk(y, model, fix_shift = FALSE, tau, pmax, method, par = NULL, nsim)

Arguments

y: a time series. Currently a numeric vector.
model: an object inheriting from class "MixAR".
fix_shift: whether the shift/mean parameter should be kept fixed to its starting value or not. Default is FALSE.
tau: tuning parameters for Metropolis-Hastings algorithm in sampling AR coefficients.
pmax: the largest autoregressive order allowed for each component.
method: character vector of length 1. Method for calculating probability of new AR order to be increased/decreased by 1 unit. Currently available "Ratio", "Poisson" and "NULL". Default is "NULL".
par: numeric, parameter for tuning probabilities according to method. Ignored if method is "NULL".
nsim: numeric, the number of iterations.

Value

out: a dataframe with g+1 columns. The first g columns contain the autoregressive orders of the components, the last column how often a model is preferred, divided by nsim.
fix_shift: the choice made for the shift/mean parameters.
method: the method used to increase/decrease AR orders.

References

ravagli2020bayesianmixAR

Author

Davide Ravagli

Note

Choose_pk currenlty supports class "MixARGaussian" only.

Examples

model <- new("MixARGaussian", 
             prob   = exampleModels$WL_At@prob,      # c(0.5, 0.5)
             scale  = exampleModels$WL_At@scale,     # c(1, 2)        
             arcoef = list(-0.5, 1) )
             # note: arcoef != list(-0.5, 1.1) == exampleModels$WL_At@arcoef@a

set.seed(1234)
n <- 50 # 200
y <- mixAR_sim(model, n, rep(0, max(model@order)), nskip = 100)

nsim <- 25 # 100
pk_star <- Choose_pk(y, model, tau = c(.15, .25), pmax = 5, method = "NULL", nsim = nsim)